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Pengxiang Zhao

Pengxiang Zhao has 4 papers in our library, in 2026, most of them on Evaluation & benchmarks, Efficient models & compression; often with Guangyi Liu, WenHao Wang, Hao Wang.

4 papers in our library · 2026

OpenAlex · h-index 1 · 3 works · 2 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 14 times in our library · ⭐ Paper Stars ranking: #9326

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 10The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 3 worksHuman-Computer Interaction · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 1 workEngineering · 1 workComputational Theory and Mathematics · 1 workComputer Vision and Pattern Recognition · 1 workElectrical and Electronic Engineering · 1 work

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 2 worksEfficient models & compression · 1 work

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026 · 9 citations)
    Mobile GUI agents built on large language models often fail in rare, app-specific situations that pre-training or fine-tuning cannot fully cover.
  2. MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026 · 6 citations)
    Mobile agents driven by multimodal LLMs usually act only through the interface: they tap, swipe and type step by step. This is flexible but slow. Shortcuts such as APIs, deep-links and RPA scripts can skip whole sequences of GUI steps, but no benchmark has systematically tested agents that mix the two.
  3. Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026 · 1 citation)
    The paper asks whether Huawei's HiFloat number formats, built for Ascend NPUs, work well for post-training quantization (PTQ) of large language models.
  4. FedGUI: Benchmarking Federated GUI Agents across Heterogeneous Platforms, Devices, and Operating Systems (2026)
    GUI agents built on vision–language models usually depend on centrally gathered, manually labelled interaction data, which is expensive and hard to scale.

 Co-authors here

12 of 18 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 14 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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